What can multi-angle dynamic light scattering (MADLS) tell you about LNP samples and when should you use it?

After recent research investigated the characterization of viral and non-viral vectors using multiple Malvern Panalytical technologies, this application note zooms in on how researchers used the Zetasizer Ultra (an advanced light scattering system) to characterize several important physical attributes in two mRNA-LNP formulations. 


After recent research investigated the characterization of viral and non-viral vectors using multiple Malvern Panalytical technologies, this application note zooms in on how researchers used the Zetasizer Ultra (an advanced light scattering system) to characterize several important physical attributes in two mRNA-LNP formulations. 

What are lipid nanoparticles?

Usually spherical in form, LNPs consist of a lipid core matrix, which itself typically consists of an ionizable cationic lipid, PEGylated lipid, and cholesterol (Figure 1). These all help stabilize and protect the LNPs in different biological environments – which is important since, as drug delivery vehicles, LNPs can transport hydrophobic or hydrophilic molecules, including small molecules, proteins, and nucleic acids.ii Naturally, our capacity to understand their physical attributes is essential for gaining regulatory approval. 

[LNP-diagram-image.png] LNP-diagram-image.png

Figure 1. Schematic representation of the basic structure and compositions of an mRNA-LNP characterized in this study.

Using MADLS in LNP research and manufacture

LNPs have recently made a name for themselves owing to their successful role in COVID-19 mRNA vaccines, protecting and transporting mRNA to cells effectively. However, we need a thorough biophysical understanding of these systems to ensure their safety, stability, and efficacy – and to secure regulatory approval. Dynamic light scattering (DLS) is one of the accepted tools to help with the biophysical characterization of LNPs by providing information about particle size, and aggregate formation. Multi-angle dynamic light scattering (MADLS) goes a step further: while traditional single-angle backscattering only provides information on particle size, multi-angle detection generates a higher resolution particle size distribution (PSD) and particle concentration data.

What can MADLS tell us about mRNA-LNP formulations?

Two important physical attributes are the particle size distributions (PSDs) of LNPs and their polydispersity. DLS measures fluctuations in the scattering intensity of particles undergoing Brownian motion at a single angle (often back scatter or side scatter angle) and by analyzing the fluctuations with cumulants or non-negative least squares (NNLS), DLS data provides information on the average diameter (Z-average), Polydispersity index (PdI), or the PSD within the sample. Observing changes in mean particle size (Z-average), and polydispersity index (PdI) is commonly used for monitoring stability of LNP preparations. 

MADLS is a more advanced option than single-angle DLS, using three different angles of detection (front, back, and side) and produces a PSD measurement that accounts for angle-dependent scattering information and as a result enables a higher-resolution picture of particle size distribution (PSD) in samples that contain multiple size populations. Plus, MADLS allows us to calculate the total particle concentration for each population in a sample.iii

Materials and methods

The study used two mRNA-LNP formulations: LNP1 and LNP2. DLS and MADLS measurements were made with a Zetasizer Ultra, using a HeNe laser at a wavelength of 632.8 nm and a maximum power of 10 mW. The single angle DLS data is from the MADLS measurement, as it is one of the three angles measured, and you get access to all angles in the measurement. The single angle DLS data (NIBS, Figure 1, blue) is taken from the MADLS measurement, as it is one of the three angles measured, and is plotted together with the combined result from all three angles in the MADLS measurement (Figure 1, red). Five replicate measurements of each sample were made, using backscatter detection and a low-volume quartz batch cuvette (ZEN2112 Malvern Panalytical). Thanks to the system’s integrated ZS XPLORER software, the instrument settings were optimized automatically. 

[Figure 2 AN240416-madls-lnp-samples.jpg] Figure 2 AN240416-madls-lnp-samples.jpg

Figure 2. Intensity-weighted PSDs of mRNA-LNP formulations, (LNP 1, A ; LNP 2, B) measured by NIBS (blue) and MADLS (red) using Zetasizer Ultra Red.

For LNP 1, the PSD obtained using DLS shows two populations of particles within the sample (Figure 2a). In contrast, MADLS, with a higher PSD size resolution than traditional NIBS, identifies three distinct peaks in the PSD. Furthermore, for LNP 2 DLS shows a single population represented by a broad peak, while MADLS identifies a smaller population of larger aggregates (Figure 2b). These larger aggregates are known to contribute to the broadening of backscatter DLS data. 

Polydispersity, meanwhile, can be used in conjunction with the average size to describe the presence of aggregates or agglomerates. Looking at the PdI and %Pd can reveal the difference in polydispersity between LNP1 and LNP2. In this context, the guideline for a population to be considered monodisperse is a %Pd of less than 20% and a PdI of ± 0.13

Delivery vector (number of repeat measurements)Polydispersity index (PdI) (%Pd)
LNP 1 (5)0.325 ± 0.0041a (57)
LNP 2 (5)0.159 ± 0.0171a (40)

Table 1. Polydispersity of mRNA-LNP formulations determined by DLS.

In this case, we can see that LNP1 is a more polydisperse sample than LNP2 (Table 1). The PSD differences are clear from the MADLS data (Figure 2; red), giving us a clear insight into the presence of multiple populations within the sample as well as showing peaks for larger aggregates. 

This is a good example of how MADLS and DLS can play an important role as a pre-screening indicator to identify the PSD of aggregates within a sample. MADLS also allows us to measure particle concentrations between ~109 and ~1012 particles/mL – which extends the concentration range of other techniques such as nanoparticle tracking analysis (NTAiv)3 – and can provide an orthogonal measurement of particle concentrations over a broad range of concentrations. Given that DLS and MADLS are often used to quickly determine particle size, polydispersity, and/or concentrations, this function is important for the development of LNPs because it can help assess the influence of formulation or processing steps on sample stability.


Assessing physical characteristics, such as particle size distribution and particle concentration, is key to the effective development of LNPs as drug delivery vehicles. From this study, we can see the value of techniques such as DLS and MADLS in giving insights into the potential instability of a sample, for instance in terms of aggregation formation over time (see Table 2 for an overview of the measurable attributes and related parameters). 

Both methods are also routinely used to understand consistency from one batch to another. DLS and MADLS can both cover a wide range of particle sizes (~1 nm to 10 μm), giving them an edge over other techniques. On the one hand, DLS is better suited for samples with relatively low polydispersity, where another population of larger particles may be present. MADLS, meanwhile, uses detection from multiple angles to give a higher PSD resolution that provides more information about the potential populations within a sample. 

Although MADLS can also be used as a screening tool for the detection of small populations of larger aggregates present in a sample (see Figure 2B), it can’t resolve small populations with a smaller size difference than 2:1 (3:1 for DLS). It can, however, be used as an orthogonal verification for other particle concentration measurement techniques (e.g., NTA, enzyme-linked immunosorbent assay (ELISA), etc.). 

AttributeMeasurement techniquesMeasurand-Parameter Abbreviation (unit)Sample information requiredSample applicability
PSDDLSZ-average diameter-Dh (nm)Dispersant viscosity and refractive indexFrom 0.3 nm to 10-20 μm 1
MADLSHydrodynamic diameter - Dh (nm)Dispersant viscosity and refractive index, Particle absorbance and refractive indexFrom 0.3 nm to 500 μm 1
PolydispersityDLS (cumulants analysis)Polydispersity Index - PdIDispersant viscosity and refractive indexSame as size measurements
DLS (NNLS analysis)Peak polydispersity - %PdDispersant viscosity and refractive index (for volume and number transformations:
Particle concentration /
Viral capsid titer
MADLSParticle concentration (particles per mL)Dispersant viscosity and refractive index, particle absorbance and refractive index
1 Depends on particle material

Table 2: Sample attributes measured by DLS and MADLS, with corresponding measured parameters and required sample information.

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  • i Markova, N.;  Cairns, S.;  Jankevics-Jones, H.;  Kaszuba, M.;  Caputo, F.; Parot, J., Biophysical Characterization of Viral and Lipid-Based Vectors for Vaccines and Therapeutics with Light Scattering and Calorimetric Techniques. Vaccines (Basel) 2021, 10 (1).
  • ii Tenchov, R.;  Bird, R.;  Curtze, A. E.; Zhou, Q., Lipid Nanoparticles─From Liposomes to mRNA Vaccine Delivery, a Landscape of Research Diversity and Advancement. ACS Nano 2021, 15 (11), 16982-17015.
  • iii Austin, J.;  Minelli, C.;  Hamilton, D.;  Wywijas, M.; Jones, H. J., Nanoparticle number concentration measurements by multi-angle dynamic light scattering. Journal of Nanoparticle Research 2020, 22 (5), 108.
  • iv N. Markova, et al. Vaccines 10, 49 (2022).


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